CE37/Canonical

Important

This information is shared by CE37 use cases across all offerings.

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Titles and Taxonomy

Main Title	Subtitle	Taxonomy	Product Category	Draft	Published	Edit
Genesys Predictive Engagement	Use machine learning powered journey analytics to observe website activity, predict visitor outcomes, and proactively engage with prospects and customers via agent-assisted chat, content offer or chatbot.	Customer Engagement	Digital	No draft	GenesysCloud GenesysEngage-onpremises PureConnect	Edit

Canonical Information

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Platform Challenge and Solution

Platform Challenge: It’s challenging to identify the right individual, the best moments, and the optimal ways to offer assistance online. Companies want to shape their customers’ journeys and drive them towards desirable outcomes, but it’s hard to utilize all of the available data in a way that is meaningful and actionable. In addition, consumers expect fast answers, but it's expensive to always engage an agent.

Platform Solution: Proactively lead customers to successful journeys on your website. Apply machine learning, dynamic personas, and outcome probabilities to identify the right moments for proactive engagement via a web chat or help content screen-pop.

Platform Benefits

The following benefits are based on benchmark information captured from Genesys customers and may vary based on industry or lines of business:

Canonical Benefit	Explanation
Improved Employee Productivity	Representatives are empowered with real time customer journey data which allows them to personalize and prioritize engagements with prospective and existing customers.
Increased Revenue	Accelerate sales and conversion rates by engaging online shoppers in real time at the right time as they browse your website. Grow customer lifetime value through more proactive and personalized service.
Reduced Handle Time	When the engagement requires escalation from self-service to assisted service, the agent is provided context of the journey.

High Level Flow

High Level Flow Steps

1. A customer is browsing a website
2. Their online journey is tracked to monitor if they need assistance
3. The system predicts the right moment to engage with the customer
4. The customer is offered chat via a chatbot or a help content screen pop
5. If required, the customer can be transferred to an agent chat screen
6. The customer is connected to an agent

Data Sheet Image

Canonical Sales Content

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Personas

• Chief Digital Officer
• Head of Contact Center(s)
• Head of Customer Experience
• Head of Customer Service

Qualifying Questions

What insights do you have about the behaviors that determine whether someone on your website is lost or unable to complete a service task? How do you know when to engage with online customers to provide support and assistance? Which channels of engagement can you use today to proactively to engage customers on your website?

Pain Points (Business Context)

• Low self-service rate on website leading to high number of contacts into contact center
• Inability to see, understand and engage with customers during service journeys across channels in real time
• Website user journeys not optimized for efficient engagement through self-service

Desired State - How to Fix It

• Use journey analytics to detect where customers struggle on a website and use this information to improve their service journeys
• Identify the user’s persona, monitor their web behavior, and predict their outcome score related to the customer service process
• Use machine learning to profile behavior, predict outcomes and allow organizations to define rules on when to intervene
• Proactively engage with customers via a chatbot if this score drops below a defined threshold while on the website
• Deflect from live agent contact by proactively displaying additional information or offering the most cost-effective channel